Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
NC, MBE (Joint) |
2024-03-12 14:45 |
Tokyo |
The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Visualization of the learning process of ResNet revealing its learning dynamics Ryodo Yuge, Takashi Shinozaki (Kindai Univ.) NC2023-59 |
We visualize the impact of skip connections, a key element in residual networks (ResNet), and visualize its impact on th... [more] |
NC2023-59 p.94 |
EMM, BioX, ISEC, SITE, ICSS, HWS, IPSJ-CSEC, IPSJ-SPT [detail] |
2023-07-25 09:00 |
Hokkaido |
Hokkaido Jichiro Kaikan |
CNN-Based Iris Recognition Using Multi-spectral Iris Images Ryosuke Kuroda, Tetsuya Honda, Hironobu Takano (Toyama Prefectural Univ.) ISEC2023-36 SITE2023-30 BioX2023-39 HWS2023-36 ICSS2023-33 EMM2023-36 |
Iris recognition using a near-infrared camera is generally known as a biometric authentication method with high accuracy... [more] |
ISEC2023-36 SITE2023-30 BioX2023-39 HWS2023-36 ICSS2023-33 EMM2023-36 pp.147-151 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-21 15:45 |
Hokkaido |
Hokkaido Univ. |
A Residual U-Net Architecture for Shuttlecock Detection Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Detection of fast-moving shuttlecocks is essential for badminton video analysis. Several methods based on deep learning ... [more] |
|
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] |
2022-05-20 17:00 |
Kumamoto |
Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Deformable registration of 3D medical images with Deep Residual UNet Taiga Nakamura, Yuki Sato, Hiroyuki Kudo, Hotaka Takizawa (Univ. of Tsukuba) SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 |
(To be available after the conference date) [more] |
SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 pp.156-160 |
MBE, NC (Joint) |
2021-10-29 11:40 |
Online |
Online |
A numerical study on the relationship between complexity and accuracy of neural networks based on ordinary differential equations Kaoru Esashika, Jun Ohkubo (Saitama Univ.) NC2021-26 |
In recent years, many reports have been published on deep neural networks. The residual networks have contributed to rem... [more] |
NC2021-26 pp.46-50 |
IMQ |
2021-10-22 13:45 |
Osaka |
Osaka Univ. |
A Tiny Convolutional Neural Network for Image Super-Resolution Kazuya Urazoe, Nobutaka Kuroki, Yu Kato, Shinya Ohtani (Kobe Univ.), Tetsuya Hirose (Osaka Univ.), Masahiro Numa (Kobe Univ.) IMQ2021-7 |
This paper surveys three techniques for reducing computational costs of convolutional neural network (CNN) for image sup... [more] |
IMQ2021-7 pp.2-7 |
ET |
2021-09-10 13:15 |
Online |
Online |
Predicting Code Reading Test Answers by Using Eye Movement Features YUE YAN, Minoru Nakayama (Tokyo Tech) ET2021-11 |
Code reading comprehension progress has been shown to distribute in the eye movement data, which makes predict the code ... [more] |
ET2021-11 pp.17-22 |
IBISML |
2021-03-03 11:15 |
Online |
Online |
IBISML2020-46 |
Developing a profitable trading strategy is a central problem in the financial industry. In this presentation, we develo... [more] |
IBISML2020-46 p.38 |
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2020-02-27 14:15 |
Hokkaido |
Hokkaido Univ. (Cancelled but technical report was issued) |
A note on detection of distress regions in subway tunnels by using U-net based network An Wang, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ) |
This paper presents an automated distress region detection method using subway tunnel images. We previously proposed a m... [more] |
|
ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] |
2019-02-20 13:00 |
Hokkaido |
Hokkaido Univ. |
A note on automatic malignant tumor candidate detection based on a 3D deep residual network with FDG-PET/CT images Zongyao Li, Ren Togo, Takahiro Ogawa, Kenji Hirata, Osamu Manabe, Tohru Shiga, Miki Haseyama (Hokkaido Univ.) |
In this paper, we propose a malignant tumor candidate detection method with FDG-PET/CT images. We design our network bas... [more] |
|
IE |
2018-06-29 10:20 |
Okinawa |
|
Single-image Rain Removal Using Residual Deep Learning Takuro Matsui, Masaaki Ikehara, Takanori Fujisawa (Keio Univ.) IE2018-23 |
Most outdoor vision systems can be influenced by rainy weather conditions. In this paper, we address a rain removal prob... [more] |
IE2018-23 pp.13-18 |
PRMU, MI, IE, SIP |
2018-05-18 10:15 |
Gifu |
|
Electronic Cleansing for CT Colonography using Deep Learning Rie Tachibana (NIT, Oshima College), Janne J. Nappi, Toru Hironaka, Hiroyuki Yoshida (MGH/HMS) SIP2018-8 IE2018-8 PRMU2018-8 MI2018-8 |
Although colonoscopy is considered as a standard procedure for colon cancer screening, CT colonography (CTC) has recentl... [more] |
SIP2018-8 IE2018-8 PRMU2018-8 MI2018-8 pp.35-37 |
PRMU |
2017-10-12 13:30 |
Kumamoto |
|
Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise (Osaka Pref. Univ.) PRMU2017-72 |
(To be available after the conference date) [more] |
PRMU2017-72 pp.55-60 |
SANE |
2017-08-24 13:50 |
Osaka |
OIT UMEDA Campus |
Deep Learning for Target Classification from SAR Imagery
-- Data Augmentation and Translation Invariance -- Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-30 |
This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (... [more] |
SANE2017-30 pp.13-17 |
PRMU, CNR |
2017-02-18 10:55 |
Hokkaido |
|
PRMU2016-158 CNR2016-25 |
(To be available after the conference date) [more] |
PRMU2016-158 CNR2016-25 pp.35-40 |